If you survey enough people, all of the advice will cancel to zero. - Naval Ravikant
In 2013, I was getting ready to turn down the promotion that would change my life.
I had spent three years diligently handling baggage at the airport until I got my big break. My boss asked me to take on a management role. It was an opportunity, but it came with tradeoffs. I needed advice from someone I trusted. I was 24 years old and still living at home with my parents. So I figured I’d ask my dad.
He told me to turn it down.
What was his logic? The promotion would force me to leave my labour union.
He was focused on the downside risk. He’d come to this country with next-to-nothing and job loss meant ruin. Protection from arbitrary firing was priced high for him. A labour union meant job security grew with years of service. I would be throwing three of those years away.
This was a big decision so I sought a second opinion from my best friend.
He told me to take the job.
He was instead focused on the upside reward. Management meant upward mobility if I continually demonstrated my worth. He was a young entrepreneur, so his advice reflected his worldview just as much my dad’s reflected his.
So who should I have listened to?
Tech Guru Naval Ravikant thinks that advice is a math function— addition to be precise:
If you survey enough people, all of the advice will cancel to zero.
This offhand remark hints at a replicable formula and I’m inclined to backtest it.
Assumptions:
Advice can be converted into numerical values to cast votes on a binary (yes/no) decision
The pre-advice baseline rests at 0 (read: no advice)
‘No’ votes are negative (read: -1) while ‘Yes’ votes are positive (+1)
The addition of all votes sums to an action bias
However, over a large enough sample size, the chance the equation will output 0 becomes a statistical certainty
So, let’s convert my conundrum into a binary question.
Question: Should I take the job?
Answers: +1 (Yes) or -1 (No)
My results:
Dad: -1
Bestie: +1
Well, that’s not helpful.
But I still made a call in 2013 on just these two inputs. How?
Well, it turns out the numerical value can exceed -1 or +1. All one needs to do is be emphatic.
My dad’s advice was certainly a No, but it was a soft no.
My bestie’s advice was a hell yes.
His conviction impacted the equation. The numerical value of his Yes was closer to +5:
Now that’s a definitive answer.
But what if I asked more people? This is where Naval’s formula shines.
Most folks I knew were in my labour union too. They might have all given me the safe answer - the soft no. ‘Stay put, don’t risk it.’ They might’ve not wanted to stick their neck out. So it’s conceivable that I would’ve solicited four additional weak ‘No’s in a row:
Asking six people has as much utility as asking no one.
As for me, I took the job and it paid off. I earn 135% more than if I’d stayed put.
So what does this teach us in the end?
One emphatic answer beats multiple lukewarm answers
Be very selective on who and how many you ask for advice
Be ruthless in shortening your consultation period before acting
People who love you the most can lead you astray because, advice says more about them than it does about you
The optimal number of advisors is closer to 1 than to n